Sold by: MphasisÂ
Deployed on AWS
A Natural Language Processing (NLP) based solution to identify key topics present in credit card specific customer complaints.
Overview
Topic Modelling based solution for customer complaints about Credit Card services. The solution clusters customer complaints into topics based on the narratives provided. Given a set of customer complaints narratives, this solution identifies for each complaint the top three issues mentioned in the narrative. The task of identifying what credit card specific issue is being mentioned in a customer complaint requires human involvement and the same is being automated with this solution.
Highlights
- The solution takes customer complaints narratives as input and predicts the top three categories of complaints thereby automating the task which previously required manual classification. This solution has been trained on customer complaint databases.
- The solution is specifically trained on customers complaints narratives pertaining to credit card issues using natural language processing.
- Need customized HyperGraf Machine Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the version 1.2
Additional details
Inputs
- Summary
- The input dataset should be in CSV format.
- The input can be provided as CSV file, with a single column called Text, with different rows containing different customer complaints.
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Topic_Modeling_of_Credit_Card_Complaints/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Topic_Modeling_of_Credit_Card_Complaints/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Text | Credit Cards Specific Customer Complaints | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
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